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enc_ac_strategy.cc
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enc_ac_strategy.cc
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// Copyright (c) the JPEG XL Project Authors. All rights reserved.
//
// Use of this source code is governed by a BSD-style
// license that can be found in the LICENSE file.
#include "lib/jxl/enc_ac_strategy.h"
#include <stdint.h>
#include <string.h>
#include <algorithm>
#include <cmath>
#include <cstdio>
#undef HWY_TARGET_INCLUDE
#define HWY_TARGET_INCLUDE "lib/jxl/enc_ac_strategy.cc"
#include <hwy/foreach_target.h>
#include <hwy/highway.h>
#include "lib/jxl/ac_strategy.h"
#include "lib/jxl/ans_params.h"
#include "lib/jxl/base/bits.h"
#include "lib/jxl/base/compiler_specific.h"
#include "lib/jxl/base/profiler.h"
#include "lib/jxl/base/status.h"
#include "lib/jxl/coeff_order_fwd.h"
#include "lib/jxl/convolve.h"
#include "lib/jxl/dct_scales.h"
#include "lib/jxl/enc_params.h"
#include "lib/jxl/enc_transforms-inl.h"
#include "lib/jxl/entropy_coder.h"
#include "lib/jxl/fast_math-inl.h"
// Some of the floating point constants in this file and in other
// files in the libjxl project have been obtained using the
// tools/optimizer/simplex_fork.py tool. It is a variation of
// Nelder-Mead optimization, and we generally try to minimize
// BPP * pnorm aggregate as reported by the benchmark_xl tool,
// but occasionally the values are optimized by using additional
// constraints such as maintaining a certain density, or ratio of
// popularity of integral transforms. Jyrki visually reviews all
// such changes and often makes manual changes to maintain good
// visual quality to changes where butteraugli was not sufficiently
// sensitive to some kind of degradation. Unfortunately image quality
// is still more of an art than science.
// This must come before the begin/end_target, but HWY_ONCE is only true
// after that, so use an "include guard".
#ifndef LIB_JXL_ENC_AC_STRATEGY_
#define LIB_JXL_ENC_AC_STRATEGY_
// Parameters of the heuristic are marked with a OPTIMIZE comment.
namespace jxl {
// Debugging utilities.
// Returns a linear sRGB color (as bytes) for each AC strategy.
const uint8_t* TypeColor(const uint8_t& raw_strategy) {
JXL_ASSERT(AcStrategy::IsRawStrategyValid(raw_strategy));
static_assert(AcStrategy::kNumValidStrategies == 27, "Change colors");
static constexpr uint8_t kColors[][3] = {
{0xFF, 0xFF, 0x00}, // DCT8
{0xFF, 0x80, 0x80}, // HORNUSS
{0xFF, 0x80, 0x80}, // DCT2x2
{0xFF, 0x80, 0x80}, // DCT4x4
{0x80, 0xFF, 0x00}, // DCT16x16
{0x00, 0xC0, 0x00}, // DCT32x32
{0xC0, 0xFF, 0x00}, // DCT16x8
{0xC0, 0xFF, 0x00}, // DCT8x16
{0x00, 0xFF, 0x00}, // DCT32x8
{0x00, 0xFF, 0x00}, // DCT8x32
{0x00, 0xFF, 0x00}, // DCT32x16
{0x00, 0xFF, 0x00}, // DCT16x32
{0xFF, 0x80, 0x00}, // DCT4x8
{0xFF, 0x80, 0x00}, // DCT8x4
{0xFF, 0xFF, 0x80}, // AFV0
{0xFF, 0xFF, 0x80}, // AFV1
{0xFF, 0xFF, 0x80}, // AFV2
{0xFF, 0xFF, 0x80}, // AFV3
{0x00, 0xC0, 0xFF}, // DCT64x64
{0x00, 0xFF, 0xFF}, // DCT64x32
{0x00, 0xFF, 0xFF}, // DCT32x64
{0x00, 0x40, 0xFF}, // DCT128x128
{0x00, 0x80, 0xFF}, // DCT128x64
{0x00, 0x80, 0xFF}, // DCT64x128
{0x00, 0x00, 0xC0}, // DCT256x256
{0x00, 0x00, 0xFF}, // DCT256x128
{0x00, 0x00, 0xFF}, // DCT128x256
};
return kColors[raw_strategy];
}
const uint8_t* TypeMask(const uint8_t& raw_strategy) {
JXL_ASSERT(AcStrategy::IsRawStrategyValid(raw_strategy));
static_assert(AcStrategy::kNumValidStrategies == 27, "Add masks");
// implicitly, first row and column is made dark
static constexpr uint8_t kMask[][64] = {
{
0, 0, 0, 0, 0, 0, 0, 0, //
0, 0, 0, 0, 0, 0, 0, 0, //
0, 0, 0, 0, 0, 0, 0, 0, //
0, 0, 0, 0, 0, 0, 0, 0, //
0, 0, 0, 0, 0, 0, 0, 0, //
0, 0, 0, 0, 0, 0, 0, 0, //
0, 0, 0, 0, 0, 0, 0, 0, //
0, 0, 0, 0, 0, 0, 0, 0, //
}, // DCT8
{
0, 0, 0, 0, 0, 0, 0, 0, //
0, 0, 0, 0, 0, 0, 0, 0, //
0, 0, 1, 0, 0, 1, 0, 0, //
0, 0, 1, 0, 0, 1, 0, 0, //
0, 0, 1, 1, 1, 1, 0, 0, //
0, 0, 1, 0, 0, 1, 0, 0, //
0, 0, 1, 0, 0, 1, 0, 0, //
0, 0, 0, 0, 0, 0, 0, 0, //
}, // HORNUSS
{
1, 1, 1, 1, 1, 1, 1, 1, //
1, 0, 1, 0, 1, 0, 1, 0, //
1, 1, 1, 1, 1, 1, 1, 1, //
1, 0, 1, 0, 1, 0, 1, 0, //
1, 1, 1, 1, 1, 1, 1, 1, //
1, 0, 1, 0, 1, 0, 1, 0, //
1, 1, 1, 1, 1, 1, 1, 1, //
1, 0, 1, 0, 1, 0, 1, 0, //
}, // 2x2
{
0, 0, 0, 0, 1, 0, 0, 0, //
0, 0, 0, 0, 1, 0, 0, 0, //
0, 0, 0, 0, 1, 0, 0, 0, //
0, 0, 0, 0, 1, 0, 0, 0, //
1, 1, 1, 1, 1, 1, 1, 1, //
0, 0, 0, 0, 1, 0, 0, 0, //
0, 0, 0, 0, 1, 0, 0, 0, //
0, 0, 0, 0, 1, 0, 0, 0, //
}, // 4x4
{}, // DCT16x16 (unused)
{}, // DCT32x32 (unused)
{}, // DCT16x8 (unused)
{}, // DCT8x16 (unused)
{}, // DCT32x8 (unused)
{}, // DCT8x32 (unused)
{}, // DCT32x16 (unused)
{}, // DCT16x32 (unused)
{
0, 0, 0, 0, 0, 0, 0, 0, //
0, 0, 0, 0, 0, 0, 0, 0, //
0, 0, 0, 0, 0, 0, 0, 0, //
0, 0, 0, 0, 0, 0, 0, 0, //
1, 1, 1, 1, 1, 1, 1, 1, //
0, 0, 0, 0, 0, 0, 0, 0, //
0, 0, 0, 0, 0, 0, 0, 0, //
0, 0, 0, 0, 0, 0, 0, 0, //
}, // DCT4x8
{
0, 0, 0, 0, 1, 0, 0, 0, //
0, 0, 0, 0, 1, 0, 0, 0, //
0, 0, 0, 0, 1, 0, 0, 0, //
0, 0, 0, 0, 1, 0, 0, 0, //
0, 0, 0, 0, 1, 0, 0, 0, //
0, 0, 0, 0, 1, 0, 0, 0, //
0, 0, 0, 0, 1, 0, 0, 0, //
0, 0, 0, 0, 1, 0, 0, 0, //
}, // DCT8x4
{
1, 1, 1, 1, 1, 0, 0, 0, //
1, 1, 1, 1, 0, 0, 0, 0, //
1, 1, 1, 0, 0, 0, 0, 0, //
1, 1, 0, 0, 0, 0, 0, 0, //
1, 0, 0, 0, 0, 0, 0, 0, //
0, 0, 0, 0, 0, 0, 0, 0, //
0, 0, 0, 0, 0, 0, 0, 0, //
0, 0, 0, 0, 0, 0, 0, 0, //
}, // AFV0
{
0, 0, 0, 0, 1, 1, 1, 1, //
0, 0, 0, 0, 0, 1, 1, 1, //
0, 0, 0, 0, 0, 0, 1, 1, //
0, 0, 0, 0, 0, 0, 0, 1, //
0, 0, 0, 0, 0, 0, 0, 0, //
0, 0, 0, 0, 0, 0, 0, 0, //
0, 0, 0, 0, 0, 0, 0, 0, //
0, 0, 0, 0, 0, 0, 0, 0, //
}, // AFV1
{
0, 0, 0, 0, 0, 0, 0, 0, //
0, 0, 0, 0, 0, 0, 0, 0, //
0, 0, 0, 0, 0, 0, 0, 0, //
0, 0, 0, 0, 0, 0, 0, 0, //
1, 0, 0, 0, 0, 0, 0, 0, //
1, 1, 0, 0, 0, 0, 0, 0, //
1, 1, 1, 0, 0, 0, 0, 0, //
1, 1, 1, 1, 0, 0, 0, 0, //
}, // AFV2
{
0, 0, 0, 0, 0, 0, 0, 0, //
0, 0, 0, 0, 0, 0, 0, 0, //
0, 0, 0, 0, 0, 0, 0, 0, //
0, 0, 0, 0, 0, 0, 0, 0, //
0, 0, 0, 0, 0, 0, 0, 0, //
0, 0, 0, 0, 0, 0, 0, 1, //
0, 0, 0, 0, 0, 0, 1, 1, //
0, 0, 0, 0, 0, 1, 1, 1, //
}, // AFV3
};
return kMask[raw_strategy];
}
void DumpAcStrategy(const AcStrategyImage& ac_strategy, size_t xsize,
size_t ysize, const char* tag, AuxOut* aux_out) {
Image3F color_acs(xsize, ysize);
for (size_t y = 0; y < ysize; y++) {
float* JXL_RESTRICT rows[3] = {
color_acs.PlaneRow(0, y),
color_acs.PlaneRow(1, y),
color_acs.PlaneRow(2, y),
};
const AcStrategyRow acs_row = ac_strategy.ConstRow(y / kBlockDim);
for (size_t x = 0; x < xsize; x++) {
AcStrategy acs = acs_row[x / kBlockDim];
const uint8_t* JXL_RESTRICT color = TypeColor(acs.RawStrategy());
for (size_t c = 0; c < 3; c++) {
rows[c][x] = color[c] / 255.f;
}
}
}
size_t stride = color_acs.PixelsPerRow();
for (size_t c = 0; c < 3; c++) {
for (size_t by = 0; by < DivCeil(ysize, kBlockDim); by++) {
float* JXL_RESTRICT row = color_acs.PlaneRow(c, by * kBlockDim);
const AcStrategyRow acs_row = ac_strategy.ConstRow(by);
for (size_t bx = 0; bx < DivCeil(xsize, kBlockDim); bx++) {
AcStrategy acs = acs_row[bx];
if (!acs.IsFirstBlock()) continue;
const uint8_t* JXL_RESTRICT color = TypeColor(acs.RawStrategy());
const uint8_t* JXL_RESTRICT mask = TypeMask(acs.RawStrategy());
if (acs.covered_blocks_x() == 1 && acs.covered_blocks_y() == 1) {
for (size_t iy = 0; iy < kBlockDim && by * kBlockDim + iy < ysize;
iy++) {
for (size_t ix = 0; ix < kBlockDim && bx * kBlockDim + ix < xsize;
ix++) {
if (mask[iy * kBlockDim + ix]) {
row[iy * stride + bx * kBlockDim + ix] = color[c] / 800.f;
}
}
}
}
// draw block edges
for (size_t ix = 0; ix < kBlockDim * acs.covered_blocks_x() &&
bx * kBlockDim + ix < xsize;
ix++) {
row[0 * stride + bx * kBlockDim + ix] = color[c] / 350.f;
}
for (size_t iy = 0; iy < kBlockDim * acs.covered_blocks_y() &&
by * kBlockDim + iy < ysize;
iy++) {
row[iy * stride + bx * kBlockDim + 0] = color[c] / 350.f;
}
}
}
}
aux_out->DumpImage(tag, color_acs);
}
} // namespace jxl
#endif // LIB_JXL_ENC_AC_STRATEGY_
HWY_BEFORE_NAMESPACE();
namespace jxl {
namespace HWY_NAMESPACE {
bool MultiBlockTransformCrossesHorizontalBoundary(
const AcStrategyImage& ac_strategy, size_t start_x, size_t y,
size_t end_x) {
if (start_x >= ac_strategy.xsize() || y >= ac_strategy.ysize()) {
return false;
}
if (y % 8 == 0) {
// Nothing crosses 64x64 boundaries, and the memory on the other side
// of the 64x64 block may still uninitialized.
return false;
}
end_x = std::min(end_x, ac_strategy.xsize());
// The first multiblock might be before the start_x, let's adjust it
// to point to the first IsFirstBlock() == true block we find by backward
// tracing.
AcStrategyRow row = ac_strategy.ConstRow(y);
const size_t start_x_limit = start_x & ~7;
while (start_x != start_x_limit && !row[start_x].IsFirstBlock()) {
--start_x;
}
for (size_t x = start_x; x < end_x;) {
if (row[x].IsFirstBlock()) {
x += row[x].covered_blocks_x();
} else {
return true;
}
}
return false;
}
bool MultiBlockTransformCrossesVerticalBoundary(
const AcStrategyImage& ac_strategy, size_t x, size_t start_y,
size_t end_y) {
if (x >= ac_strategy.xsize() || start_y >= ac_strategy.ysize()) {
return false;
}
if (x % 8 == 0) {
// Nothing crosses 64x64 boundaries, and the memory on the other side
// of the 64x64 block may still uninitialized.
return false;
}
end_y = std::min(end_y, ac_strategy.ysize());
// The first multiblock might be before the start_y, let's adjust it
// to point to the first IsFirstBlock() == true block we find by backward
// tracing.
const size_t start_y_limit = start_y & ~7;
while (start_y != start_y_limit &&
!ac_strategy.ConstRow(start_y)[x].IsFirstBlock()) {
--start_y;
}
for (size_t y = start_y; y < end_y;) {
AcStrategyRow row = ac_strategy.ConstRow(y);
if (row[x].IsFirstBlock()) {
y += row[x].covered_blocks_y();
} else {
return true;
}
}
return false;
}
float EstimateEntropy(const AcStrategy& acs, size_t x, size_t y,
const ACSConfig& config,
const float* JXL_RESTRICT cmap_factors, float* block,
float* scratch_space, uint32_t* quantized) {
const size_t size = (1 << acs.log2_covered_blocks()) * kDCTBlockSize;
// Apply transform.
for (size_t c = 0; c < 3; c++) {
float* JXL_RESTRICT block_c = block + size * c;
TransformFromPixels(acs.Strategy(), &config.Pixel(c, x, y),
config.src_stride, block_c, scratch_space);
}
HWY_FULL(float) df;
const size_t num_blocks = acs.covered_blocks_x() * acs.covered_blocks_y();
float quant_norm8 = 0;
float masking = 0;
if (num_blocks == 1) {
// When it is only one 8x8, we don't need aggregation of values.
quant_norm8 = config.Quant(x / 8, y / 8);
masking = 2.0f * config.Masking(x / 8, y / 8);
} else if (num_blocks == 2) {
// Taking max instead of 8th norm seems to work
// better for smallest blocks up to 16x8. Jyrki couldn't get
// improvements in trying the same for 16x16 blocks.
if (acs.covered_blocks_y() == 2) {
quant_norm8 =
std::max(config.Quant(x / 8, y / 8), config.Quant(x / 8, y / 8 + 1));
masking = 2.0f * std::max(config.Masking(x / 8, y / 8),
config.Masking(x / 8, y / 8 + 1));
} else {
quant_norm8 =
std::max(config.Quant(x / 8, y / 8), config.Quant(x / 8 + 1, y / 8));
masking = 2.0f * std::max(config.Masking(x / 8, y / 8),
config.Masking(x / 8 + 1, y / 8));
}
} else {
float masking_norm2 = 0;
float masking_max = 0;
// Load QF value, calculate empirical heuristic on masking field
// for weighting the information loss. Information loss manifests
// itself as ringing, and masking could hide it.
for (size_t iy = 0; iy < acs.covered_blocks_y(); iy++) {
for (size_t ix = 0; ix < acs.covered_blocks_x(); ix++) {
float qval = config.Quant(x / 8 + ix, y / 8 + iy);
qval *= qval;
qval *= qval;
quant_norm8 += qval * qval;
float maskval = config.Masking(x / 8 + ix, y / 8 + iy);
masking_max = std::max<float>(masking_max, maskval);
masking_norm2 += maskval * maskval;
}
}
quant_norm8 /= num_blocks;
quant_norm8 = FastPowf(quant_norm8, 1.0f / 8.0f);
masking_norm2 = sqrt(masking_norm2 / num_blocks);
// This is a highly empirical formula.
masking = (masking_norm2 + masking_max);
}
const auto q = Set(df, quant_norm8);
// Compute entropy.
float entropy = config.base_entropy;
auto info_loss = Zero(df);
auto info_loss2 = Zero(df);
for (size_t c = 0; c < 3; c++) {
const float* inv_matrix = config.dequant->InvMatrix(acs.RawStrategy(), c);
const auto cmap_factor = Set(df, cmap_factors[c]);
auto entropy_v = Zero(df);
auto nzeros_v = Zero(df);
auto cost1 = Set(df, config.cost1);
auto cost2 = Set(df, config.cost2);
auto cost_delta = Set(df, config.cost_delta);
for (size_t i = 0; i < num_blocks * kDCTBlockSize; i += Lanes(df)) {
const auto in = Load(df, block + c * size + i);
const auto in_y = Load(df, block + size + i) * cmap_factor;
const auto im = Load(df, inv_matrix + i);
const auto val = (in - in_y) * im * q;
const auto rval = Round(val);
const auto diff = AbsDiff(val, rval);
info_loss += diff;
info_loss2 += diff * diff;
const auto q = Abs(rval);
const auto q_is_zero = q == Zero(df);
entropy_v += IfThenElseZero(q >= Set(df, 1.5f), cost2);
// We used to have q * C here, but that cost model seems to
// be punishing large values more than necessary. Sqrt tries
// to avoid large values less aggressively. Having high accuracy
// around zero is most important at low qualities, and there
// we have directly specified costs for 0, 1, and 2.
entropy_v += Sqrt(q) * cost_delta;
nzeros_v += IfThenZeroElse(q_is_zero, Set(df, 1.0f));
}
entropy_v += nzeros_v * cost1;
entropy += GetLane(SumOfLanes(df, entropy_v));
size_t num_nzeros = GetLane(SumOfLanes(df, nzeros_v));
// Add #bit of num_nonzeros, as an estimate of the cost for encoding the
// number of non-zeros of the block.
size_t nbits = CeilLog2Nonzero(num_nzeros + 1) + 1;
// Also add #bit of #bit of num_nonzeros, to estimate the ANS cost, with a
// bias.
entropy += config.zeros_mul * (CeilLog2Nonzero(nbits + 17) + nbits);
}
float ret =
entropy +
masking *
((config.info_loss_multiplier * GetLane(SumOfLanes(df, info_loss))) +
(config.info_loss_multiplier2 *
sqrt(num_blocks * GetLane(SumOfLanes(df, info_loss2)))));
return ret;
}
uint8_t FindBest8x8Transform(size_t x, size_t y, int encoding_speed_tier,
const ACSConfig& config,
const float* JXL_RESTRICT cmap_factors,
AcStrategyImage* JXL_RESTRICT ac_strategy,
float* block, float* scratch_space,
uint32_t* quantized, float* entropy_out) {
struct TransformTry8x8 {
AcStrategy::Type type;
int encoding_speed_tier_max_limit;
float entropy_add;
float entropy_mul;
};
static const TransformTry8x8 kTransforms8x8[] = {
{
AcStrategy::Type::DCT,
9,
3.0f,
0.745f,
},
{
AcStrategy::Type::DCT4X4,
5,
4.0f,
1.0179946967008329f,
},
{
AcStrategy::Type::DCT2X2,
4,
4.0f,
0.76721119707580943f,
},
{
AcStrategy::Type::DCT4X8,
5,
0.0f,
0.700754622182473063f,
},
{
AcStrategy::Type::DCT8X4,
5,
0.0f,
0.700754622182473063f,
},
{
AcStrategy::Type::IDENTITY,
5,
8.0f,
0.81217614513585534f,
},
{
AcStrategy::Type::AFV0,
4,
3.0f,
0.70086131125719425f,
},
{
AcStrategy::Type::AFV1,
4,
3.0f,
0.70086131125719425f,
},
{
AcStrategy::Type::AFV2,
4,
3.0f,
0.70086131125719425f,
},
{
AcStrategy::Type::AFV3,
4,
3.0f,
0.70086131125719425f,
},
};
double best = 1e30;
uint8_t best_tx = kTransforms8x8[0].type;
for (auto tx : kTransforms8x8) {
if (tx.encoding_speed_tier_max_limit < encoding_speed_tier) {
continue;
}
AcStrategy acs = AcStrategy::FromRawStrategy(tx.type);
float entropy = EstimateEntropy(acs, x, y, config, cmap_factors, block,
scratch_space, quantized);
entropy = tx.entropy_add + tx.entropy_mul * entropy;
if (entropy < best) {
best_tx = tx.type;
best = entropy;
}
}
*entropy_out = best;
return best_tx;
}
// bx, by addresses the 64x64 block at 8x8 subresolution
// cx, cy addresses the left, upper 8x8 block position of the candidate
// transform.
void TryMergeAcs(AcStrategy::Type acs_raw, size_t bx, size_t by, size_t cx,
size_t cy, const ACSConfig& config,
const float* JXL_RESTRICT cmap_factors,
AcStrategyImage* JXL_RESTRICT ac_strategy,
const float entropy_mul, const uint8_t candidate_priority,
uint8_t* priority, float* JXL_RESTRICT entropy_estimate,
float* block, float* scratch_space, uint32_t* quantized) {
AcStrategy acs = AcStrategy::FromRawStrategy(acs_raw);
float entropy_current = 0;
for (size_t iy = 0; iy < acs.covered_blocks_y(); ++iy) {
for (size_t ix = 0; ix < acs.covered_blocks_x(); ++ix) {
if (priority[(cy + iy) * 8 + (cx + ix)] >= candidate_priority) {
// Transform would reuse already allocated blocks and
// lead to invalid overlaps, for example DCT64X32 vs.
// DCT32X64.
return;
}
entropy_current += entropy_estimate[(cy + iy) * 8 + (cx + ix)];
}
}
float entropy_candidate =
entropy_mul * EstimateEntropy(acs, (bx + cx) * 8, (by + cy) * 8, config,
cmap_factors, block, scratch_space,
quantized);
if (entropy_candidate >= entropy_current) return;
// Accept the candidate.
for (size_t iy = 0; iy < acs.covered_blocks_y(); iy++) {
for (size_t ix = 0; ix < acs.covered_blocks_x(); ix++) {
entropy_estimate[(cy + iy) * 8 + cx + ix] = 0;
priority[(cy + iy) * 8 + cx + ix] = candidate_priority;
}
}
ac_strategy->Set(bx + cx, by + cy, acs_raw);
entropy_estimate[cy * 8 + cx] = entropy_candidate;
}
static void SetEntropyForTransform(size_t cx, size_t cy,
const AcStrategy::Type acs_raw,
float entropy,
float* JXL_RESTRICT entropy_estimate) {
const AcStrategy acs = AcStrategy::FromRawStrategy(acs_raw);
for (size_t dy = 0; dy < acs.covered_blocks_y(); ++dy) {
for (size_t dx = 0; dx < acs.covered_blocks_x(); ++dx) {
entropy_estimate[(cy + dy) * 8 + cx + dx] = 0.0;
}
}
entropy_estimate[cy * 8 + cx] = entropy;
}
AcStrategy::Type AcsSquare(size_t blocks) {
if (blocks == 2) {
return AcStrategy::Type::DCT16X16;
} else if (blocks == 4) {
return AcStrategy::Type::DCT32X32;
} else {
return AcStrategy::Type::DCT64X64;
}
}
AcStrategy::Type AcsVerticalSplit(size_t blocks) {
if (blocks == 2) {
return AcStrategy::Type::DCT16X8;
} else if (blocks == 4) {
return AcStrategy::Type::DCT32X16;
} else {
return AcStrategy::Type::DCT64X32;
}
}
AcStrategy::Type AcsHorizontalSplit(size_t blocks) {
if (blocks == 2) {
return AcStrategy::Type::DCT8X16;
} else if (blocks == 4) {
return AcStrategy::Type::DCT16X32;
} else {
return AcStrategy::Type::DCT32X64;
}
}
// The following function tries to merge smaller transforms into
// squares and the rectangles originating from a single middle division
// (horizontal or vertical) fairly.
//
// This is now generalized to concern about squares
// of blocks X blocks size, where a block is 8x8 pixels.
void FindBestFirstLevelDivisionForSquare(
size_t blocks, bool allow_square_transform, size_t bx, size_t by, size_t cx,
size_t cy, const ACSConfig& config, const float* JXL_RESTRICT cmap_factors,
AcStrategyImage* JXL_RESTRICT ac_strategy, const float entropy_mul_JXK,
const float entropy_mul_JXJ, float* JXL_RESTRICT entropy_estimate,
float* block, float* scratch_space, uint32_t* quantized) {
// We denote J for the larger dimension here, and K for the smaller.
// For example, for 32x32 block splitting, J would be 32, K 16.
const size_t blocks_half = blocks / 2;
const AcStrategy::Type acs_rawJXK = AcsVerticalSplit(blocks);
const AcStrategy::Type acs_rawKXJ = AcsHorizontalSplit(blocks);
const AcStrategy::Type acs_rawJXJ = AcsSquare(blocks);
const AcStrategy acsJXK = AcStrategy::FromRawStrategy(acs_rawJXK);
const AcStrategy acsKXJ = AcStrategy::FromRawStrategy(acs_rawKXJ);
const AcStrategy acsJXJ = AcStrategy::FromRawStrategy(acs_rawJXJ);
AcStrategyRow row0 = ac_strategy->ConstRow(by + cy + 0);
AcStrategyRow row1 = ac_strategy->ConstRow(by + cy + blocks_half);
// Let's check if we can consider a JXJ block here at all.
// This is not necessary in the basic use of hierarchically merging
// blocks in the simplest possible way, but is needed when we try other
// 'floating' options of merging, possibly after a simple hierarchical
// merge has been explored.
if (MultiBlockTransformCrossesHorizontalBoundary(*ac_strategy, bx + cx,
by + cy, bx + cx + blocks) ||
MultiBlockTransformCrossesHorizontalBoundary(
*ac_strategy, bx + cx, by + cy + blocks, bx + cx + blocks) ||
MultiBlockTransformCrossesVerticalBoundary(*ac_strategy, bx + cx, by + cy,
by + cy + blocks) ||
MultiBlockTransformCrossesVerticalBoundary(*ac_strategy, bx + cx + blocks,
by + cy, by + cy + blocks)) {
return; // not suitable for JxJ analysis, some transforms leak out.
}
// For floating transforms there may be
// already blocks selected that make either or both JXK and
// KXJ not feasible for this location.
const bool allow_JXK = !MultiBlockTransformCrossesVerticalBoundary(
*ac_strategy, bx + cx + blocks_half, by + cy, by + cy + blocks);
const bool allow_KXJ = !MultiBlockTransformCrossesHorizontalBoundary(
*ac_strategy, bx + cx, by + cy + blocks_half, bx + cx + blocks);
// Current entropies aggregated on NxN resolution.
float entropy[2][2] = {};
for (size_t dy = 0; dy < blocks; ++dy) {
for (size_t dx = 0; dx < blocks; ++dx) {
entropy[dy / blocks_half][dx / blocks_half] +=
entropy_estimate[(cy + dy) * 8 + (cx + dx)];
}
}
float entropy_JXK_left = std::numeric_limits<float>::max();
float entropy_JXK_right = std::numeric_limits<float>::max();
float entropy_KXJ_top = std::numeric_limits<float>::max();
float entropy_KXJ_bottom = std::numeric_limits<float>::max();
float entropy_JXJ = std::numeric_limits<float>::max();
if (allow_JXK) {
if (row0[bx + cx + 0].RawStrategy() != acs_rawJXK) {
entropy_JXK_left =
entropy_mul_JXK *
EstimateEntropy(acsJXK, (bx + cx + 0) * 8, (by + cy + 0) * 8, config,
cmap_factors, block, scratch_space, quantized);
}
if (row0[bx + cx + blocks_half].RawStrategy() != acs_rawJXK) {
entropy_JXK_right =
entropy_mul_JXK * EstimateEntropy(acsJXK, (bx + cx + blocks_half) * 8,
(by + cy + 0) * 8, config,
cmap_factors, block, scratch_space,
quantized);
}
}
if (allow_KXJ) {
if (row0[bx + cx].RawStrategy() != acs_rawKXJ) {
entropy_KXJ_top =
entropy_mul_JXK *
EstimateEntropy(acsKXJ, (bx + cx + 0) * 8, (by + cy + 0) * 8, config,
cmap_factors, block, scratch_space, quantized);
}
if (row1[bx + cx].RawStrategy() != acs_rawKXJ) {
entropy_KXJ_bottom =
entropy_mul_JXK * EstimateEntropy(acsKXJ, (bx + cx + 0) * 8,
(by + cy + blocks_half) * 8, config,
cmap_factors, block, scratch_space,
quantized);
}
}
if (allow_square_transform) {
// We control the exploration of the square transform separately so that
// we can turn it off at high decoding speeds for 32x32, but still allow
// exploring 16x32 and 32x16.
entropy_JXJ = entropy_mul_JXJ * EstimateEntropy(acsJXJ, (bx + cx + 0) * 8,
(by + cy + 0) * 8, config,
cmap_factors, block,
scratch_space, quantized);
}
// Test if this block should have JXK or KXJ transforms,
// because it can have only one or the other.
float costJxN = std::min(entropy_JXK_left, entropy[0][0] + entropy[1][0]) +
std::min(entropy_JXK_right, entropy[0][1] + entropy[1][1]);
float costNxJ = std::min(entropy_KXJ_top, entropy[0][0] + entropy[0][1]) +
std::min(entropy_KXJ_bottom, entropy[1][0] + entropy[1][1]);
if (entropy_JXJ < costJxN && entropy_JXJ < costNxJ) {
ac_strategy->Set(bx + cx, by + cy, acs_rawJXJ);
SetEntropyForTransform(cx, cy, acs_rawJXJ, entropy_JXJ, entropy_estimate);
} else if (costJxN < costNxJ) {
if (entropy_JXK_left < entropy[0][0] + entropy[1][0]) {
ac_strategy->Set(bx + cx, by + cy, acs_rawJXK);
SetEntropyForTransform(cx, cy, acs_rawJXK, entropy_JXK_left,
entropy_estimate);
}
if (entropy_JXK_right < entropy[0][1] + entropy[1][1]) {
ac_strategy->Set(bx + cx + blocks_half, by + cy, acs_rawJXK);
SetEntropyForTransform(cx + blocks_half, cy, acs_rawJXK,
entropy_JXK_right, entropy_estimate);
}
} else {
if (entropy_KXJ_top < entropy[0][0] + entropy[0][1]) {
ac_strategy->Set(bx + cx, by + cy, acs_rawKXJ);
SetEntropyForTransform(cx, cy, acs_rawKXJ, entropy_KXJ_top,
entropy_estimate);
}
if (entropy_KXJ_bottom < entropy[1][0] + entropy[1][1]) {
ac_strategy->Set(bx + cx, by + cy + blocks_half, acs_rawKXJ);
SetEntropyForTransform(cx, cy + blocks_half, acs_rawKXJ,
entropy_KXJ_bottom, entropy_estimate);
}
}
}
void ProcessRectACS(PassesEncoderState* JXL_RESTRICT enc_state,
const ACSConfig& config, const Rect& rect) {
// Main philosophy here:
// 1. First find best 8x8 transform for each area.
// 2. Merging them into larger transforms where possibly, but
// starting from the smallest transforms (16x8 and 8x16).
// Additional complication: 16x8 and 8x16 are considered
// simultanouesly and fairly against each other.
// We are looking at 64x64 squares since the YtoX and YtoB
// maps happen to be at that resolution, and having
// integral transforms cross these boundaries leads to
// additional complications.
const CompressParams& cparams = enc_state->cparams;
const float butteraugli_target = cparams.butteraugli_distance;
AcStrategyImage* ac_strategy = &enc_state->shared.ac_strategy;
// TODO(veluca): reuse allocations
auto mem = hwy::AllocateAligned<float>(5 * AcStrategy::kMaxCoeffArea);
auto qmem = hwy::AllocateAligned<uint32_t>(AcStrategy::kMaxCoeffArea);
uint32_t* JXL_RESTRICT quantized = qmem.get();
float* JXL_RESTRICT block = mem.get();
float* JXL_RESTRICT scratch_space = mem.get() + 3 * AcStrategy::kMaxCoeffArea;
size_t bx = rect.x0();
size_t by = rect.y0();
JXL_ASSERT(rect.xsize() <= 8);
JXL_ASSERT(rect.ysize() <= 8);
size_t tx = bx / kColorTileDimInBlocks;
size_t ty = by / kColorTileDimInBlocks;
const float cmap_factors[3] = {
enc_state->shared.cmap.YtoXRatio(
enc_state->shared.cmap.ytox_map.ConstRow(ty)[tx]),
0.0f,
enc_state->shared.cmap.YtoBRatio(
enc_state->shared.cmap.ytob_map.ConstRow(ty)[tx]),
};
if (cparams.speed_tier > SpeedTier::kHare) return;
// First compute the best 8x8 transform for each square. Later, we do not
// experiment with different combinations, but only use the best of the 8x8s
// when DCT8X8 is specified in the tree search.
// 8x8 transforms have 10 variants, but every larger transform is just a DCT.
float entropy_estimate[64] = {};
// Favor all 8x8 transforms (against 16x8 and larger transforms)) at
// low butteraugli_target distances.
static const float k8x8mul1 = -0.55;
static const float k8x8mul2 = 1.0735757687292623f;
static const float k8x8base = 1.4;
const float mul8x8 = k8x8mul2 + k8x8mul1 / (butteraugli_target + k8x8base);
for (size_t iy = 0; iy < rect.ysize(); iy++) {
for (size_t ix = 0; ix < rect.xsize(); ix++) {
float entropy = 0.0;
const uint8_t best_of_8x8s = FindBest8x8Transform(
8 * (bx + ix), 8 * (by + iy), static_cast<int>(cparams.speed_tier),
config, cmap_factors, ac_strategy, block, scratch_space, quantized,
&entropy);
ac_strategy->Set(bx + ix, by + iy,
static_cast<AcStrategy::Type>(best_of_8x8s));
entropy_estimate[iy * 8 + ix] = entropy * mul8x8;
}
}
// Merge when a larger transform is better than the previously
// searched best combination of 8x8 transforms.
struct MergeTry {
AcStrategy::Type type;
uint8_t priority;
uint8_t decoding_speed_tier_max_limit;
uint8_t encoding_speed_tier_max_limit;
float entropy_mul;
};
static const float k8X16mul1 = -0.55;
static const float k8X16mul2 = 0.9019587899705066;
static const float k8X16base = 1.6;
const float entropy_mul16X8 =
k8X16mul2 + k8X16mul1 / (butteraugli_target + k8X16base);
// const float entropy_mul16X8 = mul8X16 * 0.91195782912371126f;
static const float k16X16mul1 = -0.35;
static const float k16X16mul2 = 0.82;
static const float k16X16base = 2.0;
const float entropy_mul16X16 =
k16X16mul2 + k16X16mul1 / (butteraugli_target + k16X16base);
// const float entropy_mul16X16 = mul16X16 * 0.83183417727960129f;
static const float k32X16mul1 = -0.1;
static const float k32X16mul2 = 0.84;
static const float k32X16base = 2.5;
const float entropy_mul16X32 =
k32X16mul2 + k32X16mul1 / (butteraugli_target + k32X16base);
const float entropy_mul32X32 = 0.9;
const float entropy_mul64X64 = 1.43f;
// TODO(jyrki): Consider this feedback in further changes:
// Also effectively when the multipliers for smaller blocks are
// below 1, this raises the bar for the bigger blocks even higher
// in that sense these constants are not independent (e.g. changing
// the constant for DCT16x32 by -5% (making it more likely) also
// means that DCT32x32 becomes harder to do when starting from
// two DCT16x32s). It might be better to make them more independent,
// e.g. by not applying the multiplier when storing the new entropy
// estimates in TryMergeToACSCandidate().
const MergeTry kTransformsForMerge[9] = {
{AcStrategy::Type::DCT16X8, 2, 4, 5, entropy_mul16X8},
{AcStrategy::Type::DCT8X16, 2, 4, 5, entropy_mul16X8},
// FindBestFirstLevelDivisionForSquare looks for DCT16X16 and its
// subdivisions. {AcStrategy::Type::DCT16X16, 3, entropy_mul16X16},
{AcStrategy::Type::DCT16X32, 4, 4, 4, entropy_mul16X32},
{AcStrategy::Type::DCT32X16, 4, 4, 4, entropy_mul16X32},
// FindBestFirstLevelDivisionForSquare looks for DCT32X32 and its
// subdivisions. {AcStrategy::Type::DCT32X32, 5, 1, 5,
// 0.9822994906548809f},
// TODO(jyrki): re-enable 64x32 and 64x64 if/when possible.
{AcStrategy::Type::DCT64X32, 6, 1, 3, 1.26f},
{AcStrategy::Type::DCT32X64, 6, 1, 3, 1.26f},
// {AcStrategy::Type::DCT64X64, 8, 1, 3, 2.0846542128012948f},
};
/*
These sizes not yet included in merge heuristic:
set(AcStrategy::Type::DCT32X8, 0.0f, 2.261390410971102f);
set(AcStrategy::Type::DCT8X32, 0.0f, 2.261390410971102f);
set(AcStrategy::Type::DCT128X128, 0.0f, 1.0f);
set(AcStrategy::Type::DCT128X64, 0.0f, 0.73f);
set(AcStrategy::Type::DCT64X128, 0.0f, 0.73f);
set(AcStrategy::Type::DCT256X256, 0.0f, 1.0f);
set(AcStrategy::Type::DCT256X128, 0.0f, 0.73f);
set(AcStrategy::Type::DCT128X256, 0.0f, 0.73f);
*/
// Priority is a tricky kludge to avoid collisions so that transforms
// don't overlap.
uint8_t priority[64] = {};
for (auto tx : kTransformsForMerge) {
if (tx.decoding_speed_tier_max_limit < cparams.decoding_speed_tier) {
continue;
}
AcStrategy acs = AcStrategy::FromRawStrategy(tx.type);
for (size_t cy = 0; cy + acs.covered_blocks_y() - 1 < rect.ysize();
cy += acs.covered_blocks_y()) {
for (size_t cx = 0; cx + acs.covered_blocks_x() - 1 < rect.xsize();
cx += acs.covered_blocks_x()) {
if (cy + 7 < rect.ysize() && cx + 7 < rect.xsize()) {
if (cparams.decoding_speed_tier < 4 &&
tx.type == AcStrategy::Type::DCT32X64) {
// We handle both DCT8X16 and DCT16X8 at the same time.
if ((cy | cx) % 8 == 0) {
FindBestFirstLevelDivisionForSquare(
8, true, bx, by, cx, cy, config, cmap_factors, ac_strategy,
tx.entropy_mul, entropy_mul64X64, entropy_estimate, block,
scratch_space, quantized);
}
continue;
} else if (tx.type == AcStrategy::Type::DCT32X16) {
// We handled both DCT8X16 and DCT16X8 at the same time,
// and that is above. The last column and last row,
// when the last column or last row is odd numbered,
// are still handled by TryMergeAcs.
continue;
}
}
if ((tx.type == AcStrategy::Type::DCT16X32 && cy % 4 != 0) ||
(tx.type == AcStrategy::Type::DCT32X16 && cx % 4 != 0)) {
// already covered by FindBest32X32
continue;
}
if (cy + 3 < rect.ysize() && cx + 3 < rect.xsize()) {
if (tx.type == AcStrategy::Type::DCT16X32) {
// We handle both DCT8X16 and DCT16X8 at the same time.
bool enable_32x32 = cparams.decoding_speed_tier < 4;
if ((cy | cx) % 4 == 0) {
FindBestFirstLevelDivisionForSquare(
4, enable_32x32, bx, by, cx, cy, config, cmap_factors,
ac_strategy, tx.entropy_mul, entropy_mul32X32,
entropy_estimate, block, scratch_space, quantized);
}
continue;
} else if (tx.type == AcStrategy::Type::DCT32X16) {
// We handled both DCT8X16 and DCT16X8 at the same time,
// and that is above. The last column and last row,
// when the last column or last row is odd numbered,
// are still handled by TryMergeAcs.
continue;
}
}
if ((tx.type == AcStrategy::Type::DCT16X32 && cy % 4 != 0) ||
(tx.type == AcStrategy::Type::DCT32X16 && cx % 4 != 0)) {
// already covered by FindBest32X32
continue;
}
if (cy + 1 < rect.ysize() && cx + 1 < rect.xsize()) {
if (tx.type == AcStrategy::Type::DCT8X16) {
// We handle both DCT8X16 and DCT16X8 at the same time.
if ((cy | cx) % 2 == 0) {
FindBestFirstLevelDivisionForSquare(
2, true, bx, by, cx, cy, config, cmap_factors, ac_strategy,
tx.entropy_mul, entropy_mul16X16, entropy_estimate, block,
scratch_space, quantized);
}
continue;
} else if (tx.type == AcStrategy::Type::DCT16X8) {
// We handled both DCT8X16 and DCT16X8 at the same time,
// and that is above. The last column and last row,
// when the last column or last row is odd numbered,
// are still handled by TryMergeAcs.
continue;
}
}
if ((tx.type == AcStrategy::Type::DCT8X16 && cy % 2 == 1) ||
(tx.type == AcStrategy::Type::DCT16X8 && cx % 2 == 1)) {
// already covered by FindBestFirstLevelDivisionForSquare
continue;
}
// All other merge sizes are handled here.
// Some of the DCT16X8s and DCT8X16s will still leak through here
// when there is an odd number of 8x8 blocks, then the last row
// and column will get their DCT16X8s and DCT8X16s through the
// normal integral transform merging process.
TryMergeAcs(tx.type, bx, by, cx, cy, config, cmap_factors, ac_strategy,
tx.entropy_mul, tx.priority, &priority[0], entropy_estimate,
block, scratch_space, quantized);
}
}
}
// Here we still try to do some non-aligned matching, find a few more
// 16X8, 8X16 and 16X16s between the non-2-aligned blocks.
if (cparams.speed_tier >= SpeedTier::kHare) {
return;
}
for (int ii = 0; ii < 3; ++ii) {
for (size_t cy = 1 - (ii == 1); cy + 1 < rect.ysize(); cy += 2) {
for (size_t cx = 1 - (ii == 2); cx + 1 < rect.xsize(); cx += 2) {
FindBestFirstLevelDivisionForSquare(
2, true, bx, by, cx, cy, config, cmap_factors, ac_strategy,
entropy_mul16X8, entropy_mul16X16, entropy_estimate, block,
scratch_space, quantized);
}
}
}
}
// NOLINTNEXTLINE(google-readability-namespace-comments)
} // namespace HWY_NAMESPACE
} // namespace jxl
HWY_AFTER_NAMESPACE();
#if HWY_ONCE